store = {}
store['args']={'num_inference_samples': 10, 'available_sample_k': 5, 'seed': 267494, 'type': 'AcquisitionFunction.random', 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST_balanced with random acquisition', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'initial_percentage': 50, 'reduce_percentage': 10, 'min_remaining_percentage': 30, 'min_candidates_per_acquired_item': 100, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 'emnist_balanced_independent_random_k10_b5_267494', 'experiments_laaos': './experiment_configs/emnist_random/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=emnist_balanced_independent_random_k10_b5_267494', '--experiments_laaos=./experiment_configs/emnist_random/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.020053191489361702, 'nll': 3.856459847223253}, 'chosen_samples': [82487, 46128, 49277, 54102, 104790], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 29.874214110000025, 'batch_acquisition_elapsed_time': 0.2297497490000069})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06845744680851064, 'nll': 58.29604724964184}, 'chosen_samples': [83687, 21761, 105053, 40746, 73799], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 50.236372853000034, 'batch_acquisition_elapsed_time': 0.0027327039999818226})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.09281914893617021, 'nll': 40.83023860938626}, 'chosen_samples': [24564, 20609, 20175, 24362, 86122], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 60.202899662999926, 'batch_acquisition_elapsed_time': 0.0027201870000226336})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.1279787234042553, 'nll': 31.797181449469107}, 'chosen_samples': [75111, 32327, 55930, 81079, 38685], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 60.574459083999955, 'batch_acquisition_elapsed_time': 0.0027026960000284816})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.12936170212765957, 'nll': 32.07625143860756}, 'chosen_samples': [2747, 87196, 102364, 29066, 47158], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.84872623199999, 'batch_acquisition_elapsed_time': 0.002700247000007039})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1498936170212766, 'nll': 29.400230852226}, 'chosen_samples': [65863, 22382, 38293, 23362, 81836], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.79095055999994, 'batch_acquisition_elapsed_time': 0.0025532350000503357})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.1627127659574468, 'nll': 28.93432193113008}, 'chosen_samples': [3948, 42591, 107087, 63774, 30843], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.59286652200001, 'batch_acquisition_elapsed_time': 0.0027525519999471726})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16936170212765958, 'nll': 25.431025411840455}, 'chosen_samples': [37609, 83410, 50461, 23352, 15658], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.139769964999914, 'batch_acquisition_elapsed_time': 0.002753102000042418})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.18643617021276596, 'nll': 23.066895708227413}, 'chosen_samples': [56611, 80730, 54638, 82863, 77410], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 50.605708195999796, 'batch_acquisition_elapsed_time': 0.002814516999933403})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.20409574468085107, 'nll': 19.251527014349374}, 'chosen_samples': [17309, 77977, 22803, 77534, 72454], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.76821787300014, 'batch_acquisition_elapsed_time': 0.0027250030000232073})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1996808510638298, 'nll': 20.52699618857085}, 'chosen_samples': [51341, 53693, 34216, 75225, 105383], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.10118628400005, 'batch_acquisition_elapsed_time': 0.0027320209999288636})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2296276595744681, 'nll': 17.70962756753919}, 'chosen_samples': [61032, 73313, 58673, 64845, 60093], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.77157626200005, 'batch_acquisition_elapsed_time': 0.0025676689999727387})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.22909574468085106, 'nll': 18.880050129148554}, 'chosen_samples': [52961, 34408, 83747, 93592, 71240], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.271905758, 'batch_acquisition_elapsed_time': 0.0027188110000224697})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.23143617021276597, 'nll': 17.613993599964587}, 'chosen_samples': [84293, 105247, 85647, 53645, 75188], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 61.11674641700006, 'batch_acquisition_elapsed_time': 0.0028089870002077078})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.23893617021276595, 'nll': 17.805620435485817}, 'chosen_samples': [42600, 21202, 106705, 112730, 22207], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.469118522000144, 'batch_acquisition_elapsed_time': 0.002699203000020134})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26015957446808513, 'nll': 15.809174441662242}, 'chosen_samples': [63541, 54609, 23064, 12087, 30131], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.61156036600005, 'batch_acquisition_elapsed_time': 0.002841457999920749})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.24962765957446809, 'nll': 15.93029111230754}, 'chosen_samples': [47407, 67970, 77469, 70196, 11309], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 51.08394041199995, 'batch_acquisition_elapsed_time': 0.0026735640001334104})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2724468085106383, 'nll': 14.358580124875967}, 'chosen_samples': [42861, 54808, 24820, 110196, 12167], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.24419870299994, 'batch_acquisition_elapsed_time': 0.002618435999920621})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.26648936170212767, 'nll': 13.777055275033762}, 'chosen_samples': [107452, 71386, 61876, 50140, 38647], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.65958691499986, 'batch_acquisition_elapsed_time': 0.002764543000012054})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.27632978723404256, 'nll': 12.231197553273208}, 'chosen_samples': [39743, 106862, 36921, 67755, 85237], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.33506738599999, 'batch_acquisition_elapsed_time': 0.0027025740000681253})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28632978723404257, 'nll': 11.491656604145435}, 'chosen_samples': [66021, 30708, 51444, 51820, 94903], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.54018085500002, 'batch_acquisition_elapsed_time': 0.002735723999876427})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29132978723404257, 'nll': 11.8378016962111}, 'chosen_samples': [17929, 67076, 83297, 19500, 107710], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.37676176600007, 'batch_acquisition_elapsed_time': 0.0027990430000954802})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.30425531914893617, 'nll': 10.730189176966537}, 'chosen_samples': [109883, 68550, 92917, 9784, 105530], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.19468112300001, 'batch_acquisition_elapsed_time': 0.0026431769999817334})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3003723404255319, 'nll': 11.02361570059492}, 'chosen_samples': [32835, 20783, 20476, 109576, 52586], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.18675691899989, 'batch_acquisition_elapsed_time': 0.0026721889998952975})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3123936170212766, 'nll': 11.177027286022268}, 'chosen_samples': [109258, 19208, 43489, 63110, 20415], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.303153087000055, 'batch_acquisition_elapsed_time': 0.0024562660000810865})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3079787234042553, 'nll': 10.22825256103848}, 'chosen_samples': [56618, 10546, 101325, 37717, 17910], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.37620035600003, 'batch_acquisition_elapsed_time': 0.002730646000145498})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.32053191489361704, 'nll': 10.391935103004283}, 'chosen_samples': [107667, 75841, 100409, 5307, 37064], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.97574401299994, 'batch_acquisition_elapsed_time': 0.0027583119999690098})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.31563829787234043, 'nll': 11.62324427239914}, 'chosen_samples': [27750, 69365, 36353, 108832, 99571], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.86734307699999, 'batch_acquisition_elapsed_time': 0.002817963999859785})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.34574468085106386, 'nll': 9.2467349822778}, 'chosen_samples': [63479, 48341, 15648, 107340, 4489], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.41369505099988, 'batch_acquisition_elapsed_time': 0.002623426999889489})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3301595744680851, 'nll': 9.465469882675306}, 'chosen_samples': [35089, 90792, 61233, 46395, 67306], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.13934849899988, 'batch_acquisition_elapsed_time': 0.0027255299996795657})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3477127659574468, 'nll': 9.805555986257628}, 'chosen_samples': [87792, 62644, 10840, 72527, 51160], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.97981348900021, 'batch_acquisition_elapsed_time': 0.0027526919998308585})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3347340425531915, 'nll': 8.785104881191506}, 'chosen_samples': [72336, 52073, 61222, 108881, 24766], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.53195115100016, 'batch_acquisition_elapsed_time': 0.0027234369999860064})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38553191489361704, 'nll': 7.433065485310046}, 'chosen_samples': [54705, 26261, 45353, 9465, 9272], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.83717038399982, 'batch_acquisition_elapsed_time': 0.002707948000079341})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3460106382978723, 'nll': 8.80447242483782}, 'chosen_samples': [40883, 48391, 1962, 6404, 28164], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.69307463599989, 'batch_acquisition_elapsed_time': 0.0026504930001465254})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3659574468085106, 'nll': 7.740717926688967}, 'chosen_samples': [79691, 92790, 90026, 1166, 89336], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.6647473310004, 'batch_acquisition_elapsed_time': 0.002813359999890963})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.39228723404255317, 'nll': 7.286105503374592}, 'chosen_samples': [97226, 104949, 96847, 22451, 75936], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.998393172000306, 'batch_acquisition_elapsed_time': 0.0027137050001329044})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41047872340425534, 'nll': 7.237143562438638}, 'chosen_samples': [74101, 72291, 27110, 68960, 103001], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.240684195000085, 'batch_acquisition_elapsed_time': 0.0027311539997754153})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3981914893617021, 'nll': 7.131810388615793}, 'chosen_samples': [1586, 50421, 15789, 93021, 75411], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.31557500700001, 'batch_acquisition_elapsed_time': 0.002806651999890164})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3905851063829787, 'nll': 6.391141156861124}, 'chosen_samples': [68327, 35746, 80494, 71598, 6399], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.548035663000064, 'batch_acquisition_elapsed_time': 0.0027445569999144936})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4173936170212766, 'nll': 6.485498095816121}, 'chosen_samples': [43328, 64121, 37760, 84433, 29909], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.34841097699973, 'batch_acquisition_elapsed_time': 0.0027332300001035037})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.41914893617021276, 'nll': 6.575271277705405}, 'chosen_samples': [54617, 96487, 70416, 9013, 102787], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.06060067600038, 'batch_acquisition_elapsed_time': 0.0027718320002350083})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4059042553191489, 'nll': 6.65134233373784}, 'chosen_samples': [55942, 107508, 30819, 30062, 45725], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.94641629600028, 'batch_acquisition_elapsed_time': 0.0026843220002774615})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4146808510638298, 'nll': 6.4043658101878895}, 'chosen_samples': [23221, 45856, 21558, 50367, 36215], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.13475857200001, 'batch_acquisition_elapsed_time': 0.0027263920001132647})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4233510638297872, 'nll': 6.225934394790136}, 'chosen_samples': [21844, 32991, 86326, 19225, 6118], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.180728398000156, 'batch_acquisition_elapsed_time': 0.002759097999842197})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4244148936170213, 'nll': 5.92626293229391}, 'chosen_samples': [50164, 47386, 75076, 34488, 26078], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.77202258599982, 'batch_acquisition_elapsed_time': 0.0026856789995690633})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4275531914893617, 'nll': 6.257037531658691}, 'chosen_samples': [57753, 11507, 10798, 44127, 112522], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.44858978399998, 'batch_acquisition_elapsed_time': 0.0027207010002712195})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4348936170212766, 'nll': 5.441353991567138}, 'chosen_samples': [111174, 15466, 21519, 72282, 90817], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 42.004019584999696, 'batch_acquisition_elapsed_time': 0.0025968330000978312})
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